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Alya Alshammari and Khalil El Hindi
The combination of collaborative deep learning and Cyber-Physical Systems (CPSs) has the potential to improve decision-making, adaptability, and efficiency in dynamic and distributed environments. However, it brings privacy, communication, and resource r...
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Duy Tung Khanh Nguyen, Dung Hoang Duong, Willy Susilo, Yang-Wai Chow and The Anh Ta
Homomorphic encryption (HE) has emerged as a pivotal technology for secure neural network inference (SNNI), offering privacy-preserving computations on encrypted data. Despite active developments in this field, HE-based SNNI frameworks are impeded by thr...
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Ching-Nam Hang, Yi-Zhen Tsai, Pei-Duo Yu, Jiasi Chen and Chee-Wei Tan
The rapid global spread of the coronavirus disease (COVID-19) has severely impacted daily life worldwide. As potential solutions, various digital contact tracing (DCT) strategies have emerged to mitigate the virus?s spread while maintaining economic and ...
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Mohit Kumar, Bernhard A. Moser, Lukas Fischer and Bernhard Freudenthaler
In order to develop machine learning and deep learning models that take into account the guidelines and principles of trustworthy AI, a novel information theoretic approach is introduced in this article. A unified approach to privacy-preserving interpret...
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Rezak Aziz, Soumya Banerjee, Samia Bouzefrane and Thinh Le Vinh
The trend of the next generation of the internet has already been scrutinized by top analytics enterprises. According to Gartner investigations, it is predicted that, by 2024, 75% of the global population will have their personal data covered under priva...
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Addi Ait-Mlouk, Sadi A. Alawadi, Salman Toor and Andreas Hellander
Machine reading comprehension (MRC) of text data is a challenging task in Natural Language Processing (NLP), with a lot of ongoing research fueled by the release of the Stanford Question Answering Dataset (SQuAD) and Conversational Question Answering (Co...
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Afsana Khan, Marijn ten Thij and Anna Wilbik
Federated learning (FL) is a privacy-preserving distributed learning approach that allows multiple parties to jointly build machine learning models without disclosing sensitive data. Although FL has solved the problem of collaboration without compromisin...
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Haokun Fang and Quan Qian
Privacy protection has been an important concern with the great success of machine learning. In this paper, it proposes a multi-party privacy preserving machine learning framework, named PFMLP, based on partially homomorphic encryption and federated lear...
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Francisco José García-Peñalvo, Cristina Casado-Lumbreras, Ricardo Colomo-Palacios and Aman Yadav
Artificial intelligence applied to the educational field has a vast potential, especially after the effects worldwide of the COVID-19 pandemic. Online or blended educational modes are needed to respond to the health situation we are living in. The tutori...
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